Huang Haoda, Yan Zeping, Li Bingliang, Lu Weixiang, He Ping, Fan Lei, Wu Xiaowei, Liang Hengrui, He Jianxing
Nanfang Hospital, Southern Medical University, Guangzhou, China.
Department of Thoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Transl Lung Cancer Res. 2024 Aug 31;13(8):1816-1827. doi: 10.21037/tlcr-24-258. Epub 2024 Aug 26.
Early-stage invasive lung adenocarcinoma (ADC) characterized by a predominant micropapillary or solid pattern exhibit an elevated risk of recurrence following sub-lobar resection, thus determining histological subtype of early-stage invasive ADC prior surgery is important for formulating lobectomy or sub-lobar resection. This study aims to develop a deep learning algorithm and assess its clinical capability in distinguishing high-risk or low-risk histologic patterns in early-stage invasive ADC based on preoperative computed tomography (CT) scans.
Two retrospective cohorts were included: development cohort 1 and external test cohort 2, comprising patients diagnosed with T1 stage invasive ADC. Electronic medical records and CT scans of all patients were documented. Patients were stratified into two risk groups. High-risk group: comprising cases with a micropapillary component ≥5% or a predominant solid pattern. Low-risk group: encompassing cases with a micropapillary component <5% and an absence of a predominant solid pattern. The overall segmentation model was modified based on Mask Region-based Convolutional Neural Network (Mask-RCNN), and Residual Network 50 (ResNet50)_3D was employed for image classification.
A total of 432 patients participated in this study, with 385 cases in cohort 1 and 47 cases in cohort 2. The fine-outline results produced by the auto-segmentation model exhibited a high level of agreement with manual segmentation by human experts, yielding a mean dice coefficient of 0.86 [95% confidence interval (CI): 0.85-0.87] in cohort 1 and 0.84 (95% CI: 0.82-0.85) in cohort 2. Furthermore, the deep learning model effectively differentiated the high-risk group from the low-risk group, achieving an area under the curve (AUC) of 0.89 (95% CI: 0.88-0.90) in cohort 1. In the external validation conducted in cohort 2, the deep learning model displayed an AUC of 0.87 (95% CI: 0.84-0.88) in distinguishing the high-risk group from the low-risk group. The average diagnostic time was 16.00±3.2 seconds, with an accuracy of 0.82 (95% CI: 0.81-0.83).
We have developed a deep learning algorithm, , for the automated segmentation of pulmonary nodules and prediction of high-risk histological patterns in early-stage lung ADC based on CT scans.
以微乳头或实性为主型为特征的早期浸润性肺腺癌（ADC）在亚肺叶切除术后复发风险升高，因此在手术前确定早期浸润性ADC的组织学亚型对于制定肺叶切除术或亚肺叶切除术很重要。本研究旨在开发一种深度学习算法，并评估其基于术前计算机断层扫描（CT）区分早期浸润性ADC中高风险或低风险组织学模式的临床能力。
纳入两个回顾性队列：开发队列1和外部测试队列2，包括诊断为T1期浸润性ADC的患者。记录所有患者的电子病历和CT扫描。患者被分为两个风险组。高风险组：包括微乳头成分≥5%或实性为主型的病例。低风险组：包括微乳头成分<5%且无实性为主型的病例。基于掩膜区域卷积神经网络（Mask-RCNN）修改整体分割模型，并采用残差网络50（ResNet50）_3D进行图像分类。
共有432例患者参与本研究，队列1中有385例，队列2中有47例。自动分割模型产生的精细轮廓结果与人类专家的手动分割高度一致，队列1中的平均骰子系数为0.86 [95%置信区间（CI）：0.85 - 0.87]，队列2中为0.84（95% CI：0.82 - 0.85）。此外，深度学习模型有效地将高风险组与低风险组区分开来，队列1中的曲线下面积（AUC）为0.89（95% CI：0.88 - 0.90）。在队列2中进行的外部验证中，深度学习模型在区分高风险组与低风险组时的AUC为0.87（95% CI：0.84 - 0.88）。平均诊断时间为16.00±3.2秒，准确率为0.82（95% CI：0.81 - 0.83）。
我们开发了一种深度学习算法，用于基于CT扫描对肺结节进行自动分割，并预测早期肺ADC中的高风险组织学模式。
